🤖 AI Summary
Existing relative pose estimation algorithms incur high computational costs and rely heavily on numerous feature matches, making them ill-suited for the real-time and robustness demands of autonomous driving. This work proposes a unified and efficient framework for relative pose estimation that introduces a novel translation parameterization and a first-order rotation approximation to derive three minimal solvers tailored for ground vehicles. By integrating multi-source priors—such as IMU-provided gravity direction, rotational axis constraints during steering, and the planar motion assumption—the method substantially reduces both the required number of point correspondences and algebraic complexity. Experiments on synthetic data and the KITTI benchmark demonstrate that the proposed approach achieves a superior trade-off between accuracy and speed compared to state-of-the-art methods.
📝 Abstract
With the advancement of visual sensing systems, computer vision is playing an increasingly important role in autonomous driving and robot navigation. Relative pose estimation in multi-camera systems is essential for accurate vehicle localization and environment perception, demanding high real-time performance and robustness. Existing methods, however, often involve high computational costs and rely heavily on abundant feature matches, limiting their applicability in time-sensitive driving scenarios. To address these limitations, this paper introduces a unified framework for efficient relative pose estimation, built upon a novel translation parameterization and first-order rotation approximation. Within this framework, we propose three efficient minimal solvers specifically designed for autonomous vehicles. The first solver integrates the vertical direction prior from Inertial Measurement Units (IMUs), the second utilizes the rotation axis direction prior during steering maneuvers, and the third is designed for planar motion - a realistic assumption for ground vehicles operating on structured roads. By reducing both the minimal number of point correspondences and the algebraic complexity, our methods enable faster hypothesis generation within RANSAC-based pipelines, improving suitability for real-time systems. Extensive experiments on synthetic datasets and the KITTI autonomous driving benchmark demonstrate that the proposed solvers achieve a favorable balance between speed and accuracy compared to existing state-of-the-art algorithms.